A single agent can handle a lot. But enterprise workflows often involve multiple domains, tools, and decision-making contexts. That’s where multi-agent systems come in.
Why multiple agents?
The same reason you have multiple people on a team: specialization. A single agent that handles customer support, data analysis, and document generation will be mediocre at all three. Three specialized agents, each with focused system prompts, targeted tool access, and domain-specific memory, will be significantly better.
Multi-agent systems also enable:
- Parallel execution — multiple agents working on different subtasks simultaneously
- Separation of concerns — each agent has a clear responsibility and limited scope
- Better security — agents only have access to the tools and data they need
- Easier debugging — when something fails, you know which agent failed and why
Orchestration patterns
Sequential pipeline
The simplest pattern. Agent A does its work, passes the result to Agent B, which passes to Agent C. Good for linear workflows like document processing: extract → validate → transform → load.
Router / dispatcher
A coordinator agent receives tasks and routes them to the right specialist agent. Good for customer-facing systems where the incoming request could be anything.
Collaborative
Multiple agents work on parts of a problem and negotiate or merge their outputs. This is the most complex pattern and requires careful design to avoid conflicts and infinite loops.
The hard problems
Orchestration introduces coordination challenges that don’t exist with single agents:
- State management — how do agents share context without overwhelming each other?
- Error propagation — when one agent fails, what happens to the others?
- Resource contention — multiple agents calling the same API can cause rate limits or race conditions
- Termination — how does the system know when it’s done?
These are distributed systems problems, and they require distributed systems thinking. The frameworks help, but the architecture decisions are what make or break a multi-agent system.
Our approach
We start with the simplest architecture that could work. One agent, well-built, solves most problems. When we need multiple agents, we prefer sequential pipelines over complex choreography. We add complexity only when the use case demands it — and we always have clear observability into what every agent is doing.